1,183 research outputs found
Stochastic Dynamics of Cascading Failures in Electric-Cyber Infrastructures
Emerging smart grids consist of tightly-coupled systems, namely a power grid and a communication system. While today\u27s power grids are highly reliable and modern control and communication systems have been deployed to further enhance their reliability, historical data suggest that they are yet vulnerable to large failures. A small set of initial disturbances in power grids in conjunction with lack of effective, corrective actions in a timely manner can trigger a sequence of dependent component failures, called cascading failures. The main thrust of this dissertation is to build a probabilistic framework for modeling cascading failures in power grids while capturing their interactions with the coupled communication systems so that the risk of cascading failures in the composite complex electric-cyber infrastructures can be examined, analyzed and predicted. A scalable and analytically tractable continuous-time Markov chain model for stochastic dynamics of cascading failures in power grids is constructed while retaining key physical attributes and operating characteristics of the power grid. The key idea of the proposed framework is to simplify the state space of the complex power system while capturing the effects of the omitted variables through the transition probabilities and their parametric dependence on physical attributes and operating characteristics of the system. In particular, the effects of the interdependencies between the power grid and the communication system have been captured by a parametric formulation of the transition probabilities using Monte-Carlo simulations of cascading failures. The cascading failures are simulated with a coupled power-system simulation framework, which is also developed in this dissertation. Specifically, the probabilistic model enables the prediction of the evolution of the blackout probability in time. Furthermore, the asymptotic analysis of the blackout probability as time tends to infinity enables the calculation of the probability mass function of the blackout size, which has been shown to have a heavy tail, e.g., power-law distribution, specifically when the grid is operating under stress scenarios. A key benefit of the model is that it enables the characterization of the severity of cascading failures in terms of a set of operating characteristics of the power grid. As a generalization to the Markov chain model, a regeneration-based model for cascading failures is also developed. The regeneration-based framework is capable of modeling cascading failures in a more general setting where the probability distribution of events in the system follows an arbitrarily specified distribution with non-Markovian characteristics. Further, a novel interdependent Markov chain model is developed, which provides a general probabilistic framework for capturing the effects of interactions among interdependent infrastructures on cascading failures. A key insight obtained from this model is that interdependencies between two systems can make two individually reliable systems behave unreliably. In particular, we show that due to the interdependencies two chains with non-heavy tail asymptotic failure distribution can result in a heavy tail distribution when coupled. Lastly, another aspect of future smart grids is studied by characterizing the fundamental bounds on the information rate in the sensor network that monitors the power grid. Specifically, a distributed source coding framework is presented that enables an improved estimate of the lower bound for the minimum required communication capacity to accurately describe the state of components in the information-centric power grid. The models developed in this dissertation provide critical understanding of cascading failures in electric-cyber infrastructures and facilitate reliable and quick detection of the risk of blackouts and precursors to cascading failures. These capabilities can guide the design of efficient communication systems and cascade aware control policies for future smart grids
Stochastic Model for Power Grid Dynamics
We introduce a stochastic model that describes the quasi-static dynamics of
an electric transmission network under perturbations introduced by random load
fluctuations, random removing of system components from service, random repair
times for the failed components, and random response times to implement optimal
system corrections for removing line overloads in a damaged or stressed
transmission network. We use a linear approximation to the network flow
equations and apply linear programming techniques that optimize the dispatching
of generators and loads in order to eliminate the network overloads associated
with a damaged system. We also provide a simple model for the operator's
response to various contingency events that is not always optimal due to either
failure of the state estimation system or due to the incorrect subjective
assessment of the severity associated with these events. This further allows us
to use a game theoretic framework for casting the optimization of the
operator's response into the choice of the optimal strategy which minimizes the
operating cost. We use a simple strategy space which is the degree of tolerance
to line overloads and which is an automatic control (optimization) parameter
that can be adjusted to trade off automatic load shed without propagating
cascades versus reduced load shed and an increased risk of propagating
cascades. The tolerance parameter is chosen to describes a smooth transition
from a risk averse to a risk taken strategy...Comment: framework for a system-level analysis of the power grid from the
viewpoint of complex network
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